卫星拒绝环境下基于 VIO-UWB 的无人机群快速初始化方法

Drones Pub Date : 2024-07-22 DOI:10.3390/drones8070339
Runmin Wang, Zhongliang Deng
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引用次数: 0

摘要

在卫星信号受阻的环境中,无人机群的快速初始化是一项技术挑战,尤其是在室内或卫星信号较弱的区域,很难确定无人机群的相对位置。两种常见的初始化方法是使用相机进行联合 SLAM 初始化,这种方法会因图像特征点分析而增加通信负担;以及通过磁罗盘等设备利用先验信息获取粗略的位置关系,这种方法缺乏准确性。近年来,视觉惯性里程测量(VIO)技术取得了长足进步,提供了新的解决方案。随着计算能力的提高和 VIO 精度的增强,现在可以通过无人机的移动来建立相对位置关系。本文提出了一种针对四架以上无人机群的两阶段鲁棒初始化方法,适用于更大规模的卫星拒止场景。首先,本文分析了 Cramér-Rao 下限(CRLB)问题和集群的移动配置问题,以确定算法的最佳锚节点。随后,采用一种策略筛选出接近 CRLB 下限的锚节点,并构建了一个优化问题,通过无人机之间的相对运动和测距关系来解决锚节点之间的位置关系。该优化问题包括二次约束和线性约束,是一个具有高鲁棒性和高精度的二次约束二次编程问题(QCQP)。在解决了锚节点问题后,本文简化并改进了一种快速蜂群协同定位算法,该算法比传统的多维缩放(MDS)算法更快。理论仿真和无人机实际测试结果表明,本文提出的算法先进、优越,能有效解决卫星信号剔除条件下的无人机群初始化问题。
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Rapid Initialization Method of Unmanned Aerial Vehicle Swarm Based on VIO-UWB in Satellite Denial Environment
In environments where satellite signals are blocked, initializing UAV swarms quickly is a technical challenge, especially indoors or in areas with weak satellite signals, making it difficult to establish the relative position of the swarm. Two common methods for initialization are using the camera for joint SLAM initialization, which increases communication burden due to image feature point analysis, and obtaining a rough positional relationship using prior information through a device such as a magnetic compass, which lacks accuracy. In recent years, visual–inertial odometry (VIO) technology has significantly progressed, providing new solutions. With improved computing power and enhanced VIO accuracy, it is now possible to establish the relative position relationship through the movement of drones. This paper proposes a two-stage robust initialization method for swarms of more than four UAVs, suitable for larger-scale satellite denial scenarios. Firstly, the paper analyzes the Cramér–Rao lower bound (CRLB) problem and the moving configuration problem of the cluster to determine the optimal anchor node for the algorithm. Subsequently, a strategy is used to screen anchor nodes that are close to the lower bound of CRLB, and an optimization problem is constructed to solve the position relationship between anchor nodes through the relative motion and ranging relationship between UAVs. This optimization problem includes quadratic constraints as well as linear constraints and is a quadratically constrained quadratic programming problem (QCQP) with high robustness and high precision. After addressing the anchor node problem, this paper simplifies and improves a fast swarm cooperative positioning algorithm, which is faster than the traditional multidimensional scaling (MDS) algorithm. The results of theoretical simulations and actual UAV tests demonstrate that the proposed algorithm is advanced, superior, and effectively solves the UAV swarm initialization problem under the condition of a satellite signal rejection.
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